3 ways AI can help solve inventory management challenges

Growing businesses have various sources of inventory that require receiving and sharing their ‘available-to-promise’ picture in real-time. As such, inventory management has become increasingly complex due to accessing information on a global scale. According to Forrester, omnichannel fulfillment is a high or top priority for 94% of retailers. To effectively manage costs and buyer needs, companies must address the basic issue of tracking quantities of supply and demand at both physical locations and ecommerce sites to ensure that the inventory pictures are in sync. Ultimately, this a much larger network than what traditional inventory management systems were designed to track and manage.

Identify the inventory pain points

It’s no longer just a B2C world where one seller is engaged with multiple customers. Instead, we’re seeing a B2B world amongst the broader supply chain where multiple inventory organizations need to share or connect inventory pictures with an intuitive, seamless user experience for configuration and modification. Sharing and receiving available-to-promise inventory with, and from, specific suppliers requires access control to reduce lead times and gain multi-enterprise visibility into each source of demand and supply.

As external partners read data from an inventory database, they need to understand how much of their inventory is still at the retailer so they can better plan their production. With accurate insights, they can use data to predict propensity to buy. This critical information supplements forecasts for future inventory needs based on historical sales, contextual information, and promotional plans. It allows key leads to plan inventory purchases from suppliers based on forecasts from demand planners and changing supply and demand. The top goal of planning inventory replenishment is having enough inventory to fulfill all customer orders, while minimizing excess stock on hand. Retailers can harness this information to understand how much inventory is needed to increase incremental sales opportunities.

Accuracy is equally important. When an organization runs a national marketing campaign, not having enough inventory safety stock can directly impact revenue, customer experience and even brand reputation. At the highest level, a control tower can help ensure the accuracy of, and provide visibility into, the performance of inventory across various channels, marketplaces, and sellers. This helps identify any anomalies (low performance) or issues (delayed supply, low inventory) that course correction can proactively adjust rather than react too late.

Although architecture to connect various solutions is an initial pressing issue for most companies, there’s a clear need to interact with inventory from a business standpoint in three ways. First, addressing the problem of having inaccurate or hidden information (clear visibility) since users need to constantly monitor supply, demand and inventory levels to identify potential inventory shortages and stale inventory. Second is being able to take action in real-time – saying ‘yes’ to the sale. Finally, applying AI insights to create inventory optimization and remove manual processes.

How AI helps optimize inventory management

1. Planning inventory replenishment – Fulfillment forecasting is particularly challenging in predicting demand — even more than supply. In order to predict allocation, data science is acritical application towards historical supply and demand because there are uncertainties and anomalies associated with each data set. Anomalies come from both supply and demand data since there will be unexpected fluctuations in each. This customer-behavior-centric model needs to consider not just where, but how and when, people want to receive their products.

By applying AI, inventory management solutions can improve store inventory levels by analyzing consumer fulfillment choices and shopping behaviors.

2. Estimated time to arrival – Knowing the quantity and location of available-to-promise inventory and where it resides is critical for businesses to meet and exceed customer expectations. Being able to communicate to customers the estimated time a product will arrive is increasingly valuable and necessary in this highly competitive age, with businesses like Amazon that can offer guaranteed delivery windows. There are countless inputs to improve the accuracy of these models and retailers must be able to simulate and drill into how each calculation was reached. This will help to ensure each fulfillment decision is aligned with the business’ priorities, whether it is cost or time.

3. Safety stock management – Businesses historically set a static quantity or percentage for their inventory levels. This means defining a minimum par that is reserved for walk-in sales and is not factored into e-commerce or other channels of fulfillment. With today’s ever-evolving consumer expectations, on top of omni-channel engagements, it is no longer enough to use generalized information. Stock levels need to dynamically shift to leverage and react to incoming demand. With automated re-balancing, tarnished brand loyalty from promising inventory to customers that cannot be delivered, or fear of overselling then over-purchasing inventory and leaving money on the table are two huge problems that can be tackled. Companies like REI use predictive rules (link resides outside of ibm.com) to access in-store and warehouse inventory, going beyond linear, rules-based sourcing to meet seasonal customer and margin needs.

To achieve profitable omni-channel results, retailers need capabilities that can intelligently balance fulfillment costs against service to enhance return on investment, improve customer experience and increase repeat purchase behavior.